Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Mixture of Ensemble Empirical Mode Decomposition and GWO-SVR Model

Abstract

The remaining useful life (RUL) prediction of lithium-ion batteries (LIBs) plays a vital role in their prognostics and health management (PHM). A battery degradation model is of great significance to maintain and replace the batteries avoiding the hazards in advance to ensure the safety and reliability of an energy storage system. In this article, a novel model is developed based on an integration of ensemble empirical mode decomposition (EEMD), gray wolf optimization, and support vector regression (GWO-SVR) to predict the RUL of LIBs. A GWO-SVR model is proposed to predict the RUL of LIBs, where the GWO algorithm is utilized to optimize the SVR kernel parameters. The EEMD is employed to decouple global degradation and local regeneration in battery capacity time series to improve prediction accuracy. This design scheme captures the global degradation behavior and local regeneration phenomenon in LIBs. The experimental results on LIB from the NASA Ames Prognostics Center of Excellence (PCoE) verify that the proposed method effectively improves the accuracy of RUL prediction of LIBs.